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HUMAN-INDUCED VEGETATION DEGRADATION IN A SEMI-ARID RANGELAND

Authors: Jackson, Hasan;

HUMAN-INDUCED VEGETATION DEGRADATION IN A SEMI-ARID RANGELAND

Abstract

Current assessments of anthropogenic land degradation and its impact on vegetation at regional scales are prone to large uncertainties due to the lack of an objective, transferable, spatially and temporally explicit measure of land degradation. These uncertainties have resulted in contradictory estimates of degradation extent and severity and the role of human activities. The uncertainties limit the ability to assess the effects on the biophysical environment and effectiveness of past, current, and future policies of land use. The overall objective of the dissertation is to assess degradation in a semi-arid region at a regional scale where the process of anthropogenic land degradation is evident. Net primary productivity (NPP) is used as the primary indicator to measure degradation. It is hypothesized that land degradation resulting from human factors on the landscape irreversibly reduces NPP below the potential set by environmental conditions. It is also hypothesized that resulting reductions in NPP are distinguishable from natural, spatial and temporal, variability in NPP. The specific goals of the dissertation are to (1) identify the extent and severity of degradation using productivity as the primary surrogate, (2) compare the degradation of productivity to other known mechanisms of degradation, and (3) relate the expression of degradation to components of vegetation and varying environmental conditions. This dissertation employed the Local NPP Scaling (LNS) approach to identify patterns of anthropogenic degradation of NPP in the Burdekin Dry Tropics (BDT) region of Queensland (14 million hectares), Australia from 2000 to 2013. The method started with land classification based on the environmental factors presumed to control NPP to group pixels having similar potential NPP. Then, satellite remotely sensing data were used to compare actual NPP with its potential. The difference, in units of mass of carbon fixed in NPP per unit area per monitoring interval and per year, also its percentage of the potential, were the measures of degradation. Degradation was then compared to non-green components of vegetation (e.g. wood, stems, leaf litter, dead biomass) to determine their relationship in space and time. Finally, the symptoms of degradation were compared to land management patterns and the environmental variability (e.g. drought, non-drought conditions). Nearly 20% of the region was identified as degraded and another 7% had significant negative trends. The average annual reduction in NPP due to anthropogenic degradation was -17% of the non-degraded potential, although the severity of degradation varied substantially throughout the region. Non-green vegetation cover was strongly correlated with the inter-annual and intra-annual temporal trends of degradation. The dynamics of degradation in drought and non-drought years provided evidence of multiple stables states of degradation.

Keywords

Vegetation, Remote sensing, Computer science, 630, Dryland, Machine Learning, Degradaton, Plant sciences, Rangland, Desertification

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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